DatriseAI-first ETL

Pardot Google BigQuery

AI-first ETL from Pardot into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Pardot into Google BigQuery

Datrise syncs Pardot's prospects, campaigns, emails, forms, and engagement grades into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Pardot: B2B marketing automation on the Salesforce platform.

Google BigQuery: Serverless analytics warehouse on GCP.

How Pardot entities map to Google BigQuery

Pardot entityGoogle BigQuery objectNotes
prospectspardot_prospectsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
campaignspardot_campaignsid PK · linked to pardot_prospects
emailspardot_emailsid PK · linked to pardot_prospects
formspardot_formsid PK · linked to pardot_prospects

FAQ

How does Datrise handle Pardot's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Pardot to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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